First, deep learning-based MHE methods usually require a large amount of data to train deep models. Benefiting from the powerful feature representation capacity, deep neural networks (DNN) [1]} have dominated most computer vision fields, including MHE. DNNs have a strong need for a large quantity of annotated data, which is crucial for model performance. However, it is very costly to obtain high-resolution remote sensing images and their corresponding ground truth height values at a global scale. Thus, there is a lack of a high-resolution, highly-accurate, and large-scale annotated height estimation benchmark dataset. Moreover, for real-world applications, providing the training data under diverse imaging conditions can be effective at improving the robustness and performance of deep networks. To tackle the data scarcity problem, we resort to constructing a large-scale synthetic dataset that contains high-resolution images with accurate geometric information captured under different conditions.
